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    Gaining real-world industry experience through Break Through Tech AI at MIT

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    Taking what they learned conceptually about artificial intelligence and machine learning (ML) this year, students from across the Greater Boston area had the opportunity to apply their new skills to real-world industry projects as part of an experiential learning opportunity offered through Break Through Tech AI at MIT.

    Hosted by the MIT Schwarzman College of Computing, Break Through Tech AI is a pilot program that aims to bridge the talent gap for women and underrepresented genders in computing fields by providing skills-based training, industry-relevant portfolios, and mentoring to undergraduate students in regional metropolitan areas in order to position them more competitively for careers in data science, machine learning, and artificial intelligence.

    “Programs like Break Through Tech AI gives us opportunities to connect with other students and other institutions, and allows us to bring MIT’s values of diversity, equity, and inclusion to the learning and application in the spaces that we hold,” says Alana Anderson, assistant dean of diversity, equity, and inclusion for the MIT Schwarzman College of Computing.

    The inaugural cohort of 33 undergraduates from 18 Greater Boston-area schools, including Salem State University, Smith College, and Brandeis University, began the free, 18-month program last summer with an eight-week, online skills-based course to learn the basics of AI and machine learning. Students then split into small groups in the fall to collaborate on six machine learning challenge projects presented to them by MathWorks, MIT-IBM Watson AI Lab, and Replicate. The students dedicated five hours or more each week to meet with their teams, teaching assistants, and project advisors, including convening once a month at MIT, while juggling their regular academic course load with other daily activities and responsibilities.

    The challenges gave the undergraduates the chance to help contribute to actual projects that industry organizations are working on and to put their machine learning skills to the test. Members from each organization also served as project advisors, providing encouragement and guidance to the teams throughout.

    “Students are gaining industry experience by working closely with their project advisors,” says Aude Oliva, director of strategic industry engagement at the MIT Schwarzman College of Computing and the MIT director of the MIT-IBM Watson AI Lab. “These projects will be an add-on to their machine learning portfolio that they can share as a work example when they’re ready to apply for a job in AI.”

    Over the course of 15 weeks, teams delved into large-scale, real-world datasets to train, test, and evaluate machine learning models in a variety of contexts.

    In December, the students celebrated the fruits of their labor at a showcase event held at MIT in which the six teams gave final presentations on their AI projects. The projects not only allowed the students to build up their AI and machine learning experience, it helped to “improve their knowledge base and skills in presenting their work to both technical and nontechnical audiences,” Oliva says.

    For a project on traffic data analysis, students got trained on MATLAB, a programming and numeric computing platform developed by MathWorks, to create a model that enables decision-making in autonomous driving by predicting future vehicle trajectories. “It’s important to realize that AI is not that intelligent. It’s only as smart as you make it and that’s exactly what we tried to do,” said Brandeis University student Srishti Nautiyal as she introduced her team’s project to the audience. With companies already making autonomous vehicles from planes to trucks a reality, Nautiyal, a physics and mathematics major, shared that her team was also highly motivated to consider the ethical issues of the technology in their model for the safety of passengers, drivers, and pedestrians.

    Using census data to train a model can be tricky because they are often messy and full of holes. In a project on algorithmic fairness for the MIT-IBM Watson AI Lab, the hardest task for the team was having to clean up mountains of unorganized data in a way where they could still gain insights from them. The project — which aimed to create demonstration of fairness applied on a real dataset to evaluate and compare effectiveness of different fairness interventions and fair metric learning techniques — could eventually serve as an educational resource for data scientists interested in learning about fairness in AI and using it in their work, as well as to promote the practice of evaluating the ethical implications of machine learning models in industry.

    Other challenge projects included an ML-assisted whiteboard for nontechnical people to interact with ready-made machine learning models, and a sign language recognition model to help disabled people communicate with others. A team that worked on a visual language app set out to include over 50 languages in their model to increase access for the millions of people that are visually impaired throughout the world. According to the team, similar apps on the market currently only offer up to 23 languages. 

    Throughout the semester, students persisted and demonstrated grit in order to cross the finish line on their projects. With the final presentations marking the conclusion of the fall semester, students will return to MIT in the spring to continue their Break Through Tech AI journey to tackle another round of AI projects. This time, the students will work with Google on new machine learning challenges that will enable them to hone their AI skills even further with an eye toward launching a successful career in AI. More

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      Empowering Cambridge youth through data activism

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      For over 40 years, the Mayor’s Summer Youth Employment Program (MSYEP, or the Mayor’s Program) in Cambridge, Massachusetts, has been providing teenagers with their first work experience, but 2022 brought a new offering. Collaborating with MIT’s Personal Robots research group (PRG) and Responsible AI for Social Empowerment and Education (RAISE) this summer, MSYEP created a STEAM-focused learning site at the Institute. Eleven students joined the program to learn coding and programming skills through the lens of “Data Activism.”

      MSYEP’s partnership with MIT provides an opportunity for Cambridge high schoolers to gain exposure to more pathways for their future careers and education. The Mayor’s Program aims to respect students’ time and show the value of their work, so participants are compensated with an hourly wage as they learn workforce skills at MSYEP worksites. In conjunction with two ongoing research studies at MIT, PRG and RAISE developed the six-week Data Activism curriculum to equip students with critical-thinking skills so they feel prepared to utilize data science to challenge social injustice and empower their community.

      Rohan Kundargi, K-12 Community Outreach Administrator for MIT Office of Government and Community Relations (OGCR), says, “I see this as a model for a new type of partnership between MIT and Cambridge MSYEP. Specifically, an MIT research project that involves students from Cambridge getting paid to learn, research, and develop their own skills!”

      Cross-Cambridge collaboration

      Cambridge’s Office of Workforce Development initially contacted MIT OGCR about hosting a potential MSYEP worksite that taught Cambridge teens how to code. When Kundargi reached out to MIT pK-12 collaborators, MIT PRG’s graduate research assistant Raechel Walker proposed the Data Activism curriculum. Walker defines “data activism” as utilizing data, computing, and art to analyze how power operates in the world, challenge power, and empathize with people who are oppressed.

      Walker says, “I wanted students to feel empowered to incorporate their own expertise, talents, and interests into every activity. In order for students to fully embrace their academic abilities, they must remain comfortable with bringing their full selves into data activism.”

      As Kundargi and Walker recruited students for the Data Activism learning site, they wanted to make sure the cohort of students — the majority of whom are individuals of color — felt represented at MIT and felt they had the agency for their voice to be heard. “The pioneers in this field are people who look like them,” Walker says, speaking of well-known data activists Timnit Gebru, Rediet Abebe, and Joy Buolamwini.

      When the program began this summer, some of the students were not aware of the ways data science and artificial intelligence exacerbate systemic oppression in society, or some of the tools currently being used to mitigate those societal harms. As a result, Walker says, the students wanted to learn more about discriminatory design in every aspect of life. They were also interested in creating responsible machine learning algorithms and AI fairness metrics.

      A different side of STEAM

      The development and execution of the Data Activism curriculum contributed to Walker’s and postdoc Xiaoxue Du’s respective research at PRG. Walker is studying AI education, specifically creating and teaching data activism curricula for minoritized communities. Du’s research explores processes, assessments, and curriculum design that prepares educators to use, adapt, and integrate AI literacy curricula. Additionally, her research targets how to leverage more opportunities for students with diverse learning needs.

      The Data Activism curriculum utilizes a “libertatory computing” framework, a term Walker coined in her position paper with Professor Cynthia Breazeal, director of MIT RAISE, dean for digital learning, and head of PRG, and Eman Sherif, a then-undergraduate researcher from University of California at San Diego, titled “Liberty Computing for African American Students.” This framework ensures that students, especially minoritized students, acquire a sound racial identity, critical consciousness, collective obligation, liberation centered academic/achievement identity, as well as the activism skills to use computing to transform a multi-layered system of barriers in which racism persists. Walker says, “We encouraged students to demonstrate competency in every pillar because all of the pillars are interconnected and build upon each other.”

      Walker developed a series of interactive coding and project-based activities that focused on understanding systemic racism, utilizing data science to analyze systemic oppression, data drawing, responsible machine learning, how racism can be embedded into AI, and different AI fairness metrics.

      This was the students’ first time learning how to create data visualizations using the programming language Python and the data analysis tool Pandas. In one project meant to examine how different systems of oppression can affect different aspects of students’ own identities, students created datasets with data from their respective intersectional identities. Another activity highlighted African American achievements, where students analyzed two datasets about African American scientists, activists, artists, scholars, and athletes. Using the data visualizations, students then created zines about the African Americans who inspired them.

      RAISE hired Olivia Dias, Sophia Brady, Lina Henriquez, and Zeynep Yalcin through the MIT Undergraduate Research Opportunity Program (UROP) and PRG hired freelancer Matt Taylor to work with Walker on developing the curriculum and designing interdisciplinary experience projects. Walker and the four undergraduate researchers constructed an intersectional data analysis activity about different examples of systemic oppression. PRG also hired three high school students to test activities and offer insights about making the curriculum engaging for program participants. Throughout the program, the Data Activism team taught students in small groups, continually asked students how to improve each activity, and structured each lesson based on the students’ interests. Walker says Dias, Brady, Henriquez, and Yalcin were invaluable to cultivating a supportive classroom environment and helping students complete their projects.

      Cambridge Rindge and Latin School senior Nina works on her rubber block stamp that depicts the importance of representation in media and greater representation in the tech industry.

      Photo: Katherine Ouellette

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      Student Nina says, “It’s opened my eyes to a different side of STEM. I didn’t know what ‘data’ meant before this program, or how intersectionality can affect AI and data.” Before MSYEP, Nina took Intro to Computer Science and AP Computer Science, but she has been coding since Girls Who Code first sparked her interest in middle school. “The community was really nice. I could talk with other girls. I saw there needs to be more women in STEM, especially in coding.” Now she’s interested in applying to colleges with strong computer science programs so she can pursue a coding-related career.

      From MSYEP to the mayor’s office

      Mayor Sumbul Siddiqui visited the Data Activism learning site on Aug. 9, accompanied by Breazeal. A graduate of MSYEP herself, Siddiqui says, “Through hands-on learning through computer programming, Cambridge high school students have the unique opportunity to see themselves as data scientists. Students were able learn ways to combat discrimination that occurs through artificial intelligence.” In an Instagram post, Siddiqui also said, “I had a blast visiting the students and learning about their projects.”

      Students worked on an activity that asked them to envision how data science might be used to support marginalized communities. They transformed their answers into block-printed T-shirt designs, carving pictures of their hopes into rubber block stamps. Some students focused on the importance of data privacy, like Jacob T., who drew a birdcage to represent data stored and locked away by third party apps. He says, “I want to open that cage and restore my data to myself and see what can be done with it.”

      The subject of Cambridge Community Charter School student Jacob T.’s project was the importance of data privacy. For his T-shirt design, he drew a birdcage to represent data stored and locked away by third party apps. (From right to left:) Breazeal, Jacob T. Kiki, Raechel Walker, and Zeynep Yalcin.

      Photo: Katherine Ouellette

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      Many students wanted to see more representation in both the media they consume and across various professional fields. Nina talked about the importance of representation in media and how that could contribute to greater representation in the tech industry, while Kiki talked about encouraging more women to pursue STEM fields. Jesmin said, “I wanted to show that data science is accessible to everyone, no matter their origin or language you speak. I wrote ‘hello’ in Bangla, Arabic, and English, because I speak all three languages and they all resonate with me.”

      Student Jesmin (left) explains the concept of her T-shirt design to Mayor Siddiqui. She wants data science to be accessible to everyone, no matter their origin or language, so she drew a globe and wrote ‘hello’ in the three languages she speaks: Bangla, Arabic, and English.

      Photo: Katherine Ouellette

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      “Overall, I hope the students continue to use their data activism skills to re-envision a society that supports marginalized groups,” says Walker. “Moreover, I hope they are empowered to become data scientists and understand how their race can be a positive part of their identity.” More

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      in Data Management & Statistics

      Living better with algorithms

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      Laboratory for Information and Decision Systems (LIDS) student Sarah Cen remembers the lecture that sent her down the track to an upstream question.

      At a talk on ethical artificial intelligence, the speaker brought up a variation on the famous trolley problem, which outlines a philosophical choice between two undesirable outcomes.

      The speaker’s scenario: Say a self-driving car is traveling down a narrow alley with an elderly woman walking on one side and a small child on the other, and no way to thread between both without a fatality. Who should the car hit?

      Then the speaker said: Let’s take a step back. Is this the question we should even be asking?

      That’s when things clicked for Cen. Instead of considering the point of impact, a self-driving car could have avoided choosing between two bad outcomes by making a decision earlier on — the speaker pointed out that, when entering the alley, the car could have determined that the space was narrow and slowed to a speed that would keep everyone safe.

      Recognizing that today’s AI safety approaches often resemble the trolley problem, focusing on downstream regulation such as liability after someone is left with no good choices, Cen wondered: What if we could design better upstream and downstream safeguards to such problems? This question has informed much of Cen’s work.

      “Engineering systems are not divorced from the social systems on which they intervene,” Cen says. Ignoring this fact risks creating tools that fail to be useful when deployed or, more worryingly, that are harmful.

      Cen arrived at LIDS in 2018 via a slightly roundabout route. She first got a taste for research during her undergraduate degree at Princeton University, where she majored in mechanical engineering. For her master’s degree, she changed course, working on radar solutions in mobile robotics (primarily for self-driving cars) at Oxford University. There, she developed an interest in AI algorithms, curious about when and why they misbehave. So, she came to MIT and LIDS for her doctoral research, working with Professor Devavrat Shah in the Department of Electrical Engineering and Computer Science, for a stronger theoretical grounding in information systems.

      Auditing social media algorithms

      Together with Shah and other collaborators, Cen has worked on a wide range of projects during her time at LIDS, many of which tie directly to her interest in the interactions between humans and computational systems. In one such project, Cen studies options for regulating social media. Her recent work provides a method for translating human-readable regulations into implementable audits.

      To get a sense of what this means, suppose that regulators require that any public health content — for example, on vaccines — not be vastly different for politically left- and right-leaning users. How should auditors check that a social media platform complies with this regulation? Can a platform be made to comply with the regulation without damaging its bottom line? And how does compliance affect the actual content that users do see?

      Designing an auditing procedure is difficult in large part because there are so many stakeholders when it comes to social media. Auditors have to inspect the algorithm without accessing sensitive user data. They also have to work around tricky trade secrets, which can prevent them from getting a close look at the very algorithm that they are auditing because these algorithms are legally protected. Other considerations come into play as well, such as balancing the removal of misinformation with the protection of free speech.

      To meet these challenges, Cen and Shah developed an auditing procedure that does not need more than black-box access to the social media algorithm (which respects trade secrets), does not remove content (which avoids issues of censorship), and does not require access to users (which preserves users’ privacy).

      In their design process, the team also analyzed the properties of their auditing procedure, finding that it ensures a desirable property they call decision robustness. As good news for the platform, they show that a platform can pass the audit without sacrificing profits. Interestingly, they also found the audit naturally incentivizes the platform to show users diverse content, which is known to help reduce the spread of misinformation, counteract echo chambers, and more.

      Who gets good outcomes and who gets bad ones?

      In another line of research, Cen looks at whether people can receive good long-term outcomes when they not only compete for resources, but also don’t know upfront what resources are best for them.

      Some platforms, such as job-search platforms or ride-sharing apps, are part of what is called a matching market, which uses an algorithm to match one set of individuals (such as workers or riders) with another (such as employers or drivers). In many cases, individuals have matching preferences that they learn through trial and error. In labor markets, for example, workers learn their preferences about what kinds of jobs they want, and employers learn their preferences about the qualifications they seek from workers.

      But learning can be disrupted by competition. If workers with a particular background are repeatedly denied jobs in tech because of high competition for tech jobs, for instance, they may never get the knowledge they need to make an informed decision about whether they want to work in tech. Similarly, tech employers may never see and learn what these workers could do if they were hired.

      Cen’s work examines this interaction between learning and competition, studying whether it is possible for individuals on both sides of the matching market to walk away happy.

      Modeling such matching markets, Cen and Shah found that it is indeed possible to get to a stable outcome (workers aren’t incentivized to leave the matching market), with low regret (workers are happy with their long-term outcomes), fairness (happiness is evenly distributed), and high social welfare.

      Interestingly, it’s not obvious that it’s possible to get stability, low regret, fairness, and high social welfare simultaneously.  So another important aspect of the research was uncovering when it is possible to achieve all four criteria at once and exploring the implications of those conditions.

      What is the effect of X on Y?

      For the next few years, though, Cen plans to work on a new project, studying how to quantify the effect of an action X on an outcome Y when it’s expensive — or impossible — to measure this effect, focusing in particular on systems that have complex social behaviors.

      For instance, when Covid-19 cases surged in the pandemic, many cities had to decide what restrictions to adopt, such as mask mandates, business closures, or stay-home orders. They had to act fast and balance public health with community and business needs, public spending, and a host of other considerations.

      Typically, in order to estimate the effect of restrictions on the rate of infection, one might compare the rates of infection in areas that underwent different interventions. If one county has a mask mandate while its neighboring county does not, one might think comparing the counties’ infection rates would reveal the effectiveness of mask mandates. 

      But of course, no county exists in a vacuum. If, for instance, people from both counties gather to watch a football game in the maskless county every week, people from both counties mix. These complex interactions matter, and Sarah plans to study questions of cause and effect in such settings.

      “We’re interested in how decisions or interventions affect an outcome of interest, such as how criminal justice reform affects incarceration rates or how an ad campaign might change the public’s behaviors,” Cen says.

      Cen has also applied the principles of promoting inclusivity to her work in the MIT community.

      As one of three co-presidents of the Graduate Women in MIT EECS student group, she helped organize the inaugural GW6 research summit featuring the research of women graduate students — not only to showcase positive role models to students, but also to highlight the many successful graduate women at MIT who are not to be underestimated.

      Whether in computing or in the community, a system taking steps to address bias is one that enjoys legitimacy and trust, Cen says. “Accountability, legitimacy, trust — these principles play crucial roles in society and, ultimately, will determine which systems endure with time.”  More

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      in Data Management & Statistics

      MIT Schwarzman College of Computing unveils Break Through Tech AI

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      Aimed at driving diversity and inclusion in artificial intelligence, the MIT Stephen A. Schwarzman College of Computing is launching Break Through Tech AI, a new program to bridge the talent gap for women and underrepresented genders in AI positions in industry.

      Break Through Tech AI will provide skills-based training, industry-relevant portfolios, and mentoring to qualified undergraduate students in the Greater Boston area in order to position them more competitively for careers in data science, machine learning, and artificial intelligence. The free, 18-month program will also provide each student with a stipend for participation to lower the barrier for those typically unable to engage in an unpaid, extra-curricular educational opportunity.

      “Helping position students from diverse backgrounds to succeed in fields such as data science, machine learning, and artificial intelligence is critical for our society’s future,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “We look forward to working with students from across the Greater Boston area to provide them with skills and mentorship to help them find careers in this competitive and growing industry.”

      The college is collaborating with Break Through Tech — a national initiative launched by Cornell Tech in 2016 to increase the number of women and underrepresented groups graduating with degrees in computing — to host and administer the program locally. In addition to Boston, the inaugural artificial intelligence and machine learning program will be offered in two other metropolitan areas — one based in New York hosted by Cornell Tech and another in Los Angeles hosted by the University of California at Los Angeles Samueli School of Engineering.

      “Break Through Tech’s success at diversifying who is pursuing computer science degrees and careers has transformed lives and the industry,” says Judith Spitz, executive director of Break Through Tech. “With our new collaborators, we can apply our impactful model to drive inclusion and diversity in artificial intelligence.”

      The new program will kick off this summer at MIT with an eight-week, skills-based online course and in-person lab experience that teaches industry-relevant tools to build real-world AI solutions. Students will learn how to analyze datasets and use several common machine learning libraries to build, train, and implement their own ML models in a business context.

      Following the summer course, students will be matched with machine-learning challenge projects for which they will convene monthly at MIT and work in teams to build solutions and collaborate with an industry advisor or mentor throughout the academic year, resulting in a portfolio of resume-quality work. The participants will also be paired with young professionals in the field to help build their network, prepare their portfolio, practice for interviews, and cultivate workplace skills.

      “Leveraging the college’s strong partnership with industry, Break Through AI will offer unique opportunities to students that will enhance their portfolio in machine learning and AI,” says Asu Ozdaglar, deputy dean of academics of the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science. Ozdaglar, who will be the MIT faculty director of Break Through Tech AI, adds: “The college is committed to making computing inclusive and accessible for all. We’re thrilled to host this program at MIT for the Greater Boston area and to do what we can to help increase diversity in computing fields.”

      Break Through Tech AI is part of the MIT Schwarzman College of Computing’s focus to advance diversity, equity, and inclusion in computing. The college aims to improve and create programs and activities that broaden participation in computing classes and degree programs, increase the diversity of top faculty candidates in computing fields, and ensure that faculty search and graduate admissions processes have diverse slates of candidates and interviews.

      “By engaging in activities like Break Through Tech AI that work to improve the climate for underrepresented groups, we’re taking an important step toward creating more welcoming environments where all members can innovate and thrive,” says Alana Anderson, assistant dean for diversity, equity and inclusion for the Schwarzman College of Computing. More